01-EDA R Notebook

This notebook performs Exploratory Data Analysis on the motor vehicle crashes data aggregated with the ACS data

LOAD DATA

df <- read_csv("../data/processed/acs_mvc_combined_2018_2023.csv")
glimpse(df)

Rows: 13,984
Columns: 63
$ geoid                    <dbl> 3.6005e+10, 3.6005e+10, 3.6005e+10, 3.6005e+10, 3.6005e…
$ year                     <dbl> 2018, 2019, 2020, 2021, 2022, 2023, 2018, 2019, 2020, 2…
$ total_population         <dbl> 47900.000, 46630.000, 44881.000, 45523.000, 30541.000, …
$ pct_male_population      <dbl> 13.576200, 13.607120, 13.691763, 13.568965, 13.581743, …
$ pct_female_population    <dbl> 1.2045929, 1.1130174, 1.0137920, 1.0631988, 0.9757375, …
$ pct_white_population     <dbl> 3.7014614, 4.7887626, 5.7039727, 5.8871340, 6.2145968, …
$ pct_black_population     <dbl> 8.849687, 8.200729, 7.666941, 7.187576, 6.758128, 6.613…
$ pct_asian_population     <dbl> 0.26096033, 0.38816213, 0.38100755, 0.38881445, 0.40273…
$ pct_hispanic_population  <dbl> 4.862213, 5.161913, 5.158085, 4.758034, 3.837464, 3.557…
$ pct_foreign_born         <dbl> 2.206681, 2.365430, 2.319467, 2.220855, 1.990767, 1.626…
$ pct_age_under_18         <dbl> 0.3569937, 0.2208878, 0.2161271, 0.2152758, 0.1899086, …
$ pct_age_18_34            <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000…
$ pct_age_35_64            <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000…
$ pct_age_65_plus          <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000…
$ median_income            <dbl> 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 58582.658, 49…
$ pct_income_under_25k     <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
$ pct_income_25k_75k       <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
$ pct_income_75k_plus      <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
$ pct_below_poverty        <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
$ pct_above_poverty        <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000…
$ poverty_rate             <dbl> NA, NA, NA, NA, NA, NA, 22.633201, 22.440424, 21.358291…
$ median_gross_rent        <dbl> 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 1579.11…
$ pct_owner_occupied       <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000…
$ pct_renter_occupied      <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
$ pct_no_vehicle           <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
$ pct_one_vehicle          <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
$ pct_two_plus_vehicles    <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000…
$ pct_less_than_hs         <dbl> 6.3924843, 6.0261634, 5.7017446, 5.6608747, 4.8950591, …
$ pct_hs_diploma           <dbl> 2.0229645, 2.7364358, 3.0547448, 3.4619862, 3.8996758, …
$ pct_some_college         <dbl> 1.2526096, 1.1237401, 1.0405294, 0.9797245, 1.2540519, …
$ pct_associates_degree    <dbl> 0.1691023, 0.1737079, 0.2339520, 0.2723898, 0.2521201, …
$ pct_bachelors_degree     <dbl> 0.14822547, 0.15440703, 0.18938972, 0.23724271, 0.11132…
$ pct_graduate_degree      <dbl> 0.01670146, 0.01930088, 0.04233417, 0.05272060, 0.13424…
$ pct_in_labor_force       <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000…
$ pct_employed             <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000…
$ pct_unemployed           <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
$ pct_not_in_labor_force   <dbl> 14.780793, 14.720137, 14.705555, 14.632164, 14.557480, …
$ unemployment_rate        <dbl> NA, NA, NA, NA, NA, NA, 15.750133, 13.478747, 10.806175…
$ pct_commute_short        <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
$ pct_commute_medium       <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
$ pct_commute_long         <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000…
$ pct_drive_alone          <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000…
$ pct_carpool              <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000…
$ pct_public_transit       <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000…
$ pct_walk                 <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000…
$ pct_bike                 <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000…
$ pct_work_from_home       <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000…
$ total_crashes            <dbl> 2, 4, 0, 0, 0, 0, 43, 30, 31, 30, 17, 20, 48, 45, 39, 2…
$ persons_injured          <dbl> 1, 2, 0, 0, 0, 0, 11, 8, 12, 12, 10, 10, 10, 18, 13, 3,…
$ persons_killed           <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0…
$ pedestrians_injured      <dbl> 0, 0, 0, 0, 0, 0, 2, 2, 3, 2, 4, 1, 3, 0, 2, 0, 1, 1, 1…
$ pedestrians_killed       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ cyclists_injured         <dbl> 0, 0, 0, 0, 0, 0, 0, 2, 1, 0, 1, 1, 1, 2, 1, 0, 0, 3, 0…
$ cyclists_killed          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ motorists_injured        <dbl> 1, 2, 0, 0, 0, 0, 9, 4, 8, 10, 4, 8, 6, 16, 10, 3, 12, …
$ motorists_killed         <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0…
$ crash_rate_per_1000      <dbl> 0.04175365, 0.08578169, 0.00000000, 0.00000000, 0.00000…
$ injury_rate_per_1000     <dbl> 0.02087683, 0.04289084, 0.00000000, 0.00000000, 0.00000…
$ fatality_rate_per_1000   <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000…
$ pedestrian_rate_per_1000 <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000…
$ cyclist_rate_per_1000    <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000…
$ motorist_rate_per_1000   <dbl> 0.02087683, 0.04289084, 0.00000000, 0.00000000, 0.00000…
$ injury_fatality_ratio    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 10, NA, NA,…

CONVERT DATA TYPES

df <- df %>%
  mutate(
    geoid = as.character(geoid),
    year = as.integer(year),
    total_population = as.integer(total_population),
    total_crashes = as.integer(total_crashes),
    persons_injured = as.integer(persons_injured),
    persons_killed = as.integer(persons_killed),
    pedestrians_injured = as.integer(pedestrians_injured),
    pedestrians_killed = as.integer(pedestrians_killed),
    cyclists_injured = as.integer(cyclists_injured),
    cyclists_killed = as.integer(cyclists_killed),
    motorists_injured = as.integer(motorists_injured),
    motorists_killed = as.integer(motorists_killed)
  )
glimpse(df)

Rows: 13,984
Columns: 63
$ geoid                    <chr> "36005000100", "36005000100", "36005000100", "360050001…
$ year                     <int> 2018, 2019, 2020, 2021, 2022, 2023, 2018, 2019, 2020, 2…
$ total_population         <int> 47900, 46630, 44881, 45523, 30541, 24345, 51349, 50967,…
$ pct_male_population      <dbl> 13.576200, 13.607120, 13.691763, 13.568965, 13.581743, …
$ pct_female_population    <dbl> 1.2045929, 1.1130174, 1.0137920, 1.0631988, 0.9757375, …
$ pct_white_population     <dbl> 3.7014614, 4.7887626, 5.7039727, 5.8871340, 6.2145968, …
$ pct_black_population     <dbl> 8.849687, 8.200729, 7.666941, 7.187576, 6.758128, 6.613…
$ pct_asian_population     <dbl> 0.26096033, 0.38816213, 0.38100755, 0.38881445, 0.40273…
$ pct_hispanic_population  <dbl> 4.862213, 5.161913, 5.158085, 4.758034, 3.837464, 3.557…
$ pct_foreign_born         <dbl> 2.206681, 2.365430, 2.319467, 2.220855, 1.990767, 1.626…
$ pct_age_under_18         <dbl> 0.3569937, 0.2208878, 0.2161271, 0.2152758, 0.1899086, …
$ pct_age_18_34            <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000…
$ pct_age_35_64            <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000…
$ pct_age_65_plus          <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000…
$ median_income            <dbl> 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 58582.658, 49…
$ pct_income_under_25k     <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
$ pct_income_25k_75k       <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
$ pct_income_75k_plus      <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
$ pct_below_poverty        <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
$ pct_above_poverty        <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000…
$ poverty_rate             <dbl> NA, NA, NA, NA, NA, NA, 22.633201, 22.440424, 21.358291…
$ median_gross_rent        <dbl> 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 1579.11…
$ pct_owner_occupied       <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000…
$ pct_renter_occupied      <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
$ pct_no_vehicle           <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
$ pct_one_vehicle          <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
$ pct_two_plus_vehicles    <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000…
$ pct_less_than_hs         <dbl> 6.3924843, 6.0261634, 5.7017446, 5.6608747, 4.8950591, …
$ pct_hs_diploma           <dbl> 2.0229645, 2.7364358, 3.0547448, 3.4619862, 3.8996758, …
$ pct_some_college         <dbl> 1.2526096, 1.1237401, 1.0405294, 0.9797245, 1.2540519, …
$ pct_associates_degree    <dbl> 0.1691023, 0.1737079, 0.2339520, 0.2723898, 0.2521201, …
$ pct_bachelors_degree     <dbl> 0.14822547, 0.15440703, 0.18938972, 0.23724271, 0.11132…
$ pct_graduate_degree      <dbl> 0.01670146, 0.01930088, 0.04233417, 0.05272060, 0.13424…
$ pct_in_labor_force       <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000…
$ pct_employed             <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000…
$ pct_unemployed           <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
$ pct_not_in_labor_force   <dbl> 14.780793, 14.720137, 14.705555, 14.632164, 14.557480, …
$ unemployment_rate        <dbl> NA, NA, NA, NA, NA, NA, 15.750133, 13.478747, 10.806175…
$ pct_commute_short        <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
$ pct_commute_medium       <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
$ pct_commute_long         <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000…
$ pct_drive_alone          <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000…
$ pct_carpool              <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000…
$ pct_public_transit       <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000…
$ pct_walk                 <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000…
$ pct_bike                 <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000…
$ pct_work_from_home       <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000…
$ total_crashes            <int> 2, 4, 0, 0, 0, 0, 43, 30, 31, 30, 17, 20, 48, 45, 39, 2…
$ persons_injured          <int> 1, 2, 0, 0, 0, 0, 11, 8, 12, 12, 10, 10, 10, 18, 13, 3,…
$ persons_killed           <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0…
$ pedestrians_injured      <int> 0, 0, 0, 0, 0, 0, 2, 2, 3, 2, 4, 1, 3, 0, 2, 0, 1, 1, 1…
$ pedestrians_killed       <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ cyclists_injured         <int> 0, 0, 0, 0, 0, 0, 0, 2, 1, 0, 1, 1, 1, 2, 1, 0, 0, 3, 0…
$ cyclists_killed          <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ motorists_injured        <int> 1, 2, 0, 0, 0, 0, 9, 4, 8, 10, 4, 8, 6, 16, 10, 3, 12, …
$ motorists_killed         <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0…
$ crash_rate_per_1000      <dbl> 0.04175365, 0.08578169, 0.00000000, 0.00000000, 0.00000…
$ injury_rate_per_1000     <dbl> 0.02087683, 0.04289084, 0.00000000, 0.00000000, 0.00000…
$ fatality_rate_per_1000   <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000…
$ pedestrian_rate_per_1000 <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000…
$ cyclist_rate_per_1000    <dbl> 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000…
$ motorist_rate_per_1000   <dbl> 0.02087683, 0.04289084, 0.00000000, 0.00000000, 0.00000…
$ injury_fatality_ratio    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 10, NA, NA,…

DATA QUALITY CHECKS

# Visualize missingness
vis_miss(df)

# Count NA values per column
colSums(is.na(df))

                   geoid                     year         total_population 
                       0                        0                        0 
     pct_male_population    pct_female_population     pct_white_population 
                     426                      426                      426 
    pct_black_population     pct_asian_population  pct_hispanic_population 
                     426                      426                      426 
        pct_foreign_born         pct_age_under_18            pct_age_18_34 
                     426                      426                      426 
           pct_age_35_64          pct_age_65_plus            median_income 
                     426                      426                        0 
    pct_income_under_25k       pct_income_25k_75k      pct_income_75k_plus 
                     426                      426                      426 
       pct_below_poverty        pct_above_poverty             poverty_rate 
                     426                      426                      455 
       median_gross_rent       pct_owner_occupied      pct_renter_occupied 
                       0                      426                      426 
          pct_no_vehicle          pct_one_vehicle    pct_two_plus_vehicles 
                     426                      426                      426 
        pct_less_than_hs           pct_hs_diploma         pct_some_college 
                     426                      426                      426 
   pct_associates_degree     pct_bachelors_degree      pct_graduate_degree 
                     426                      426                      426 
      pct_in_labor_force             pct_employed           pct_unemployed 
                     426                      426                      426 
  pct_not_in_labor_force        unemployment_rate        pct_commute_short 
                     426                      464                      426 
      pct_commute_medium         pct_commute_long          pct_drive_alone 
                     426                      426                      426 
             pct_carpool       pct_public_transit                 pct_walk 
                     426                      426                      426 
                pct_bike       pct_work_from_home            total_crashes 
                     426                      426                        0 
         persons_injured           persons_killed      pedestrians_injured 
                       0                        0                        0 
      pedestrians_killed         cyclists_injured          cyclists_killed 
                       0                        0                        0 
       motorists_injured         motorists_killed      crash_rate_per_1000 
                       0                        0                      426 
    injury_rate_per_1000   fatality_rate_per_1000 pedestrian_rate_per_1000 
                     426                      426                      426 
   cyclist_rate_per_1000   motorist_rate_per_1000    injury_fatality_ratio 
                     426                      426                    12721 

Identify and remove 426 rows with extensive missingness

df$na_rate <- rowMeans(is.na(df))

# Filter out rows with high missingness
df <- df %>%
    filter(rowMeans(is.na(.)) <= 0.05)

# Drop 29 rows with poverty_rate NA and/or 38 unemployment_rate NA values
df <- df %>%
    drop_na(c(unemployment_rate, poverty_rate))

# Drop na_rate column
df <- df %>% select(-na_rate, -injury_fatality_ratio)

Second quality check

# Visualize missingness
vis_miss(df)

NA
NA

NO NA values!

Winsorization of extreme values

replace_top_99_with_median <- function(x) {
  # Ensure numeric
  if (!is.numeric(x)) stop("Column must be numeric.")
  
  # Calculate the 99th percentile and median
  upper_threshold <- quantile(x, 0.99, na.rm = TRUE)
  median_val <- median(x, na.rm = TRUE)
  
  # Replace values above the 99th percentile with the median
  x[x > upper_threshold] <- median_val
  return(x)
}

per_1000_vars <- grep("per_1000", names(df), value = TRUE)
df[per_1000_vars] <- lapply(df[per_1000_vars], replace_top_99_with_median)

Univariate Analysis

Export clean data

write_csv(df, "../data/model-ready/social-risk-crash-rate-data.csv")